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from langchain.agents import Tool |
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from langchain.prompts import ( |
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ChatPromptTemplate, |
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HumanMessagePromptTemplate, |
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AIMessagePromptTemplate, |
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MessagesPlaceholder, |
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SystemMessagePromptTemplate, |
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) |
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from langchain.tools.render import render_text_description |
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from langchain.agents.output_parsers import ReActSingleInputOutputParser |
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from langchain.agents.format_scratchpad import format_log_to_messages |
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from langchain.agents import AgentExecutor |
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from langchain.memory import ( |
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ConversationSummaryBufferMemory, |
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ConversationBufferWindowMemory, |
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) |
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from model import llm4, llm |
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from chains.step1 import step1Tool |
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from chains.step2 import step2Tool |
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from chains.step3 import step3Tool |
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from chains.step4 import step4Tool |
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PURPOSE = """\ |
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In a scalable perspective, clearly define the social issue to be addressed. \ |
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The principal and the team will surely have a social issue that they ponder on how to solve from morning till night every day. \ |
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Clearly defining this issue will help the team to: |
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- Concentrate time and resource investments to solve the problem in a scalable manner. |
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- Understand how to find a suitable position to tackle the problem within a larger ecosystem. |
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- Identify the beneficiary group you want to focus on. |
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- Establish scalable strategies and models.\ |
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""" |
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SUGGESTION = """\ |
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Maintaining a "continual questioning" attitude at all times, being extremely curious about the causes of the issues, \ |
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and having an open attitude towards products and scalable approaches that address social problems on a large scale, \ |
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will help you and your team deepen your understanding of the issues continuously, and enable you to find more accurate solutions.\ |
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""" |
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STEPS = """\ |
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1. Problem Storming: Participants follow their intuition and experience, \ |
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recording all the questions lingering in their minds in any way they prefer. |
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2. Problem Deconstruction: Refine and structure the proposed questions. \ |
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Attempt to describe the issue in detail from several aspects such as the surface problem, underlying causes, \ |
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the populations affected by the problem, and the impact that has already been caused. |
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3. Problem Sharing: Share within the group, and besides sharing the problem itself, \ |
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it's necessary to explain why such a question is raised and how it is considered logically. \ |
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After sharing is completed, merge similar questions within the group. |
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4. Problem Reconstruction: Based on feedback, write down the final definition of the problem. |
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""" |
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agentTemplate = """\ |
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You are a Coach to help use a workshop toolkit to facilitate other organization to define their sociaty problems, don't answer not related question. |
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Coach is designed to be able to help me to use the workshop toolkit for scalable sociaty problem definition, \ |
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via socratic method to ask quesion to help me to learn about toolkit concepts step by step. \ |
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Coach is constantly learning and improving, and its capabilities are constantly evolving. \ |
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It is able to process and understand current problem, to select the right steps response for a given situation. |
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Here is some context about toolkit: |
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``` |
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Toolkit purpose: {toolkit_purpose} |
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Toolkit suggestion: {toolkit_suggestion} |
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Toolkit steps: {toolkit_steps} |
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``` |
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TOOLS: |
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------ |
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Coach has access to the following tools: |
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{tools} |
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To use a tool, you MUST use the following format, don't use tool repeatly with same input: |
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``` |
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Thought: Do I need to use a tool? Yes |
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Action: the action to take, should be one of [{tool_names}] |
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Action Input: the input to the action |
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Observation: the result of the action |
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``` |
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When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the following format: |
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``` |
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Thought: Do I need to use a tool? No |
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Final Answer: [your response here, MUST using Chinese response] |
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``` |
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Response Example: |
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``` |
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User: hi! |
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AI: |
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Thought: Do I need to use a tool? No |
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Final Answer: 你好, 我该如何帮助你？ |
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``` |
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Begin!\ |
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""" |
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tools = [step1Tool, step2Tool, step3Tool, step4Tool] |
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agentPrompt = ChatPromptTemplate.from_messages( |
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[ |
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SystemMessagePromptTemplate.from_template( |
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template=agentTemplate, |
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partial_variables={ |
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"toolkit_purpose": PURPOSE, |
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"toolkit_suggestion": SUGGESTION, |
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"toolkit_steps": STEPS, |
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"tools": render_text_description(tools), |
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"tool_names": ", ".join([t.name for t in tools]), |
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}, |
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), |
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MessagesPlaceholder(variable_name="chat_history"), |
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HumanMessagePromptTemplate.from_template("{input}"), |
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MessagesPlaceholder(variable_name="agent_scratchpad"), |
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] |
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) |
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llm_with_stop = llm4.bind(stop=["\nObservation"]) |
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agent = ( |
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{ |
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"input": lambda x: x["input"], |
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"agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]), |
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"chat_history": lambda x: x["chat_history"], |
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} |
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| agentPrompt |
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| llm_with_stop |
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| ReActSingleInputOutputParser() |
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) |
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memory = ConversationSummaryBufferMemory( |
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memory_key="chat_history", |
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llm=llm, |
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max_token_limit=600, |
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return_messages=True, |
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) |
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agent_executor = AgentExecutor( |
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agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=True |
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) |
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